# -*- coding: UTF-8 -*- 
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets('MNIST_data',one_hot=True)

import tensorflow as tf

sess=tf.InteractiveSession()

# images
x=tf.placeholder(tf.float32,shape=[None,784])
# labels
y_=tf.placeholder(tf.float32,shape=[None,10])
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))

sess.run(tf.global_variables_initializer()) 

y = tf.matmul(x,W) + b 

cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) 

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

for _ in range(1000): #1000i
    batch = mnist.train.next_batch(100) #100i
    train_step.run(feed_dict={x: batch[0], y_: batch[1]}) 
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})) 
def weight_variable(shape):
    #stddev is 标准差 
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial) 
    
def bias_variable(shape): 
    initial = tf.constant(0.1, shape=shape) 
    return tf.Variable(initial) 

# convolution   
def conv2d(x, W): 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 
    
def max_pool_2x2(x): 
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# first convolutional layer
W_conv1 = weight_variable([5, 5, 1, 32]) 
b_conv1 = bias_variable([32]) 
# (40000,784) => (40000,28,28,1)
x_image = tf.reshape(x, [-1,28,28,1]) 
#print (image.get_shape()) # =>(40000,28,28,1)

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 
#print (h_conv1.get_shape()) # => (40000, 28, 28, 32)
h_pool1 = max_pool_2x2(h_conv1)
#print (h_pool1.get_shape()) # => (40000, 14, 14, 32)

# second convolutional layer
W_conv2 = weight_variable([5, 5, 32, 64]) 
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 
#print (h_conv2.get_shape()) # => (40000, 14,14, 64)
h_pool2 = max_pool_2x2(h_conv2) 
#print (h_pool2.get_shape()) # => (40000, 7, 7, 64)

# densely connected layer
W_fc1 = weight_variable([7 * 7 * 64, 1024]) 
b_fc1 = bias_variable([1024]) 

# (40000, 7, 7, 64) => (40000, 3136)
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 
#print (h_fc1.get_shape()) # => (40000, 1024)

# dropout
keep_prob = tf.placeholder(tf.float32) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 
W_fc2 = weight_variable([1024, 10]) 
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 
# cost function
cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) 
# optimisation function
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 
# evaluation
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 

saver = tf.train.Saver() # defaults to saving all variables

sess.run(tf.global_variables_initializer()) 
for i in range(20000): #10000#20000i
    batch = mnist.train.next_batch(50) 
    if i%100 == 0: 
        train_accuracy = accuracy.eval(feed_dict={ x:batch[0], y_: batch[1], keep_prob: 1.0}) 
        print("step %d, training accuracy %g"%(i, train_accuracy)) 
        
    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) 
saver.save(sess, '/home/akita/snap/tensor/model.ckpt') #save to your path 

print("test accuracy %g"%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

